Rambo's Interdisciplinary Approach to Religious Conversion: The Case of St. Augustine

This dissertation illustrates the merits of an interdisciplinary approach to religious conversion by employing Lewis Rambo’s systemic stage model to illumine the process of St. Augustine’s conversion. Previous studies of Augustine’s conversion have commonly explored his narrative of transformation from the perspective of one specific discipline, such as theology, history, or psychology. In doing so, they have necessarily restricted attention to a limited set of questions and problems. By bringing these disciplines into a structured, critical conversation, this study demonstrates how formulating and responding to the interplay among personal, social, cultural, and religious dimensions of Augustine’s conversion process may eventuate in the consideration of issues previously unarticulated and thus unaddressed. Rambo (1993) formulates a model of religious change that consists of what he calls context, crisis, quest, encounter, interaction, commitment, and consequences. Change is explained by drawing upon the research and scholarship of psychologists, sociologists, anthropologists, and religionists, in conjunction with the contributions of theologians. This study unfolds in the following chapters: I. Introduction; II. Literature review of scholarship about conversion, with emphasis on explication of Rambo’s model; III. A description of the case of Augustine, drawn from a close reading of the Confessions; IV. Literature review of scholarship about Augustine’s conversion; V. Interdisciplinary interpretation of Augustine’s conversion; and VI. Implications for scholars of conversion, and for pastoral caregivers, as well as recommendations for future research. This dissertation demonstrates how Augustine’s conversion experience was deeply influenced by 1) psychological distress and crisis; 2) the quest to know himself and the divine; 3) interactions with significant others; 4) participation in Christian communities; 5) philosophical and cultural changes; and 6) the encounter with the divine. As such, this study reveals the value of interpreting Augustine’s conversion as an evolving process constituted in multiple factors that can be differentiated from one another, yet clearly interact with one another. It examines the implications of constructing an interdisciplinary approach to Augustine’s conversion narrative for both the academy and the Christian community, and recommends the use of Rambo’s model in studies of other cases of religious change.

Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps

A new neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors. The architecture, called Fuzzy ARTMAP, achieves a synthesis of fuzzy logic and Adaptive Resonance Theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Fuzzy ARTMAP also realizes a new Minimax Learning Rule that conjointly minimizes predictive error and maximizes code compression, or generalization. This is achieved by a match tracking process that increases the ART vigilance parameter by the minimum amount needed to correct a predictive error. As a result, the system automatically learns a minimal number of recognition categories, or "hidden units", to met accuracy criteria. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy logic play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Improved prediction is achieved by training the system several times using different orderings of the input set. This voting strategy can also be used to assign probability estimates to competing predictions given small, noisy, or incomplete training sets. Four classes of simulations illustrate Fuzzy ARTMAP performance as compared to benchmark back propagation and genetic algorithm systems. These simulations include (i) finding points inside vs. outside a circle; (ii) learning to tell two spirals apart; (iii) incremental approximation of a piecewise continuous function; and (iv) a letter recognition database. The Fuzzy ARTMAP system is also compared to Salzberg's NGE system and to Simpson's FMMC system.